Hyper-Personalization in Marketing: Use Cases & Examples

Gen Z is reshaping retail with unique consumer behaviors. With their projected $12 trillion spending power by 2030, they will heavily influence what brands sell.¹ They see marketing as valuable only when it is authentic, values-driven, and empathetic, rejecting anything that feels fake, jargon-heavy, or disconnected from real people and causes.

One way to reach that audience is to deliver hyper-personalized experiences. Discover hyper-personalization in marketing, what it is, common use cases, its connection to behavioral marketing, key challenges, and practical tips to help businesses leverage it in their marketing campaigns.

What is hyper-personalization in marketing?

Hyper-personalization in marketing refers to tailoring marketing messages, offers, and customer experiences at an individual level. Unlike traditional personalization, which may use a customer’s name in an email or recommend products based on broad segments, hyper-personalization involves using advanced analytics, real-time data, and AI to deliver messages and experiences uniquely designed for each customer.

It relies on customer data, including browsing history, purchase history, location data, and contextual data, to create meaningful and personalized interactions that align with customer needs and expectations.

This approach enables businesses to maximize engagement, increase customer loyalty, and lower customer acquisition costs by delivering hyper-personalized marketing campaigns across digital channels.

AI, LLMs, and machine learning: how to leverage them in hyper-personalization in marketing

AI, machine learning, and large language models (LLMs) play a central role in hyper-personalization. They enable businesses to analyze vast amounts of customer data and extract real-time insights. For instance:

• Machine learning algorithms identify patterns in purchase behavior and browsing history to generate relevant product recommendations.
• LLMs can craft personalized messages or personalized content that resonate with individual preferences, improving conversion rates.
• Predictive analytics helps forecast customer needs based on past purchases, search data, or even payment method preferences.
• Dynamic pricing, powered by AI, enables businesses to offer personalized discounts in real-time, based on customer behavior and loyalty level.

Differences from traditional personalization

Traditional personalization typically relies on basic customer segmentation and static rules, such as sending a birthday email or suggesting items purchased by others in the same demographic. Hyper-personalization differs by:

• Using real-time insights instead of static segmentation.
• Employing advanced algorithms and machine learning to dynamically adapt personalization strategies.
• Incorporating contextual data, such as customers’ location or payment method, to tailor experiences.
• Delivering personalized interactions across multiple digital channels, ensuring a consistent customer experience.

Metrics to assess its success

The success of hyper-personalized marketing campaigns can be measured through:

• Conversion rates: Improved sales from customized messages and offers.
• Customer satisfaction: Higher ratings from customer feedback and post-purchase surveys.
• Customer retention and loyalty: Growth in repeat purchases and loyal customers.
• Customer acquisition costs: Ability to reduce customer acquisition costs by targeting precisely.
• Engagement metrics: Increases in open rates, click-through rates, and time spent engaging with personalized content.
• Customer lifetime value (CLV): A long-term measure of customer experience improvements from customized interactions.

What is behavioral marketing?

Behavioral marketing involves targeting customers based on their past interactions, such as browsing history, previous purchases, online behavior, and user behavior across digital campaigns. This approach goes beyond demographic data and focuses on actual customer behavior.

Hyper-personalization involves behavioral marketing as one of its core inputs. By analyzing behavioral data points, businesses can gain insight into customer expectations and design highly personalized marketing campaigns. For example, identifying repeat purchases or abandoned carts provides valuable data for crafting personalized rewards or personalized messages.

How to leverage behavioral marketing?

Businesses can use behavioral marketing in several ways to improve customer experience:

• Segment customers based on purchase history, browsing history, and contextual data to deliver personalized content.
• Use customer feedback and post-purchase surveys to refine the hyper-personalization strategy.
• Trigger personalized discounts or personalized rewards in response to specific actions, such as cart abandonment or frequent visits.
• Integrate data collection from first-party data and third-party data sources to develop advanced segmentation strategies.

Top 5 companies with a behavioral marketing focus

Use cases of hyper-personalization in marketing

Hyper-personalization can be applied across many marketing scenarios, where customer data and advanced algorithms transform static campaigns into dynamic, individualized experiences. Some practical applications include:

• eCommerce recommendations: Online retailers can leverage browsing history, purchase history, and customers’ location to deliver product suggestions that align with individual preferences. For example, recommending complementary products immediately after checkout or offering restock reminders based on previous purchases helps create meaningful and personalized interactions.
• Personalized rewards programs: Loyalty programs benefit from hyper-personalization by offering tailored incentives that cater to individual needs and preferences. Instead of generic point systems, businesses can reward customers with personalized content or rewards tailored to their past purchases, browsing history, or preferred payment methods.
• Digital campaigns and personalized content: Hyper-personalized marketing campaigns allow businesses to send targeted personalized messages across email, social media, and other digital channels. These campaigns reflect customer behavior and contextual data, such as recent searches or online activity, to increase conversion rates.
• Location-based offers: Retailers and hospitality providers can use location data to push hyper-personalized experiences at the right moment. Examples include personalized discounts sent when a customer is near a store or personalized messages recommending nearby restaurants to travelers.

Industries that can use hyper-personalization

• Retail and eCommerce: This sector can utilize browsing history, search data, and purchase behavior to refine product recommendations, create personalized discounts, and optimize dynamic pricing strategies.
• Banking and finance: Financial institutions can use customer segmentation and contextual data, such as transaction history and preferred payment method, to offer personalized financial products or incentives. For example, a bank might provide personalized rewards for using a specific card or tailored loan offers based on past behavior.
• Healthcare: Providers can utilize valuable data from patient histories and customer needs to deliver personalized reminders, content, or preventive care recommendations. This creates hyper-personalized experiences that improve patient engagement and adherence.
• Hospitality: Hotels and travel companies can tailor experiences using a customer’s location and booking history. Personalized messages, such as offering a room upgrade based on previous purchases or suggesting activities aligned with past preferences, can increase customer loyalty.
• Technology and SaaS: SaaS providers can employ advanced segmentation and customer behavior data to personalize onboarding experiences, create tailored content recommendations, and reduce customer acquisition costs.

What are the real-life examples?

Amazon

Amazon Personalize is a managed machine learning service that generates real-time, personalized recommendations by analyzing user behavior data, including clicks, views, and purchases. It helps businesses deliver tailored product suggestions, content feeds, and marketing messages without requiring deep expertise in AI.

Amazon Bedrock is a serverless platform that provides access to multiple foundation models through APIs, enabling developers to build and scale generative AI applications, such as chatbots, search, or content generation. It also allows secure customization with a company’s own data, making personalization flexible and privacy-conscious.²

Etsy & eBay

Etsy and eBay are adopting social-media-inspired strategies to revitalize growth by offering hyper-personalized shopping experiences. Rather than relying on traditional marketing models, they are developing algorithm-driven feeds similar to those of TikTok, Instagram, and Pinterest, designed to keep users engaged and help them discover relevant products.

Etsy uses artificial intelligence and large language models to analyze browsing behaviors, such as which items shoppers linger on, to tailor recommendations and even infer details like whether a customer is a parent or pet owner.

eBay is creating a feed-like interface and has introduced AI-generated outfit suggestions that link to matching items on its marketplace. With Etsy’s 100 million listings and eBay’s 2.3 billion, both companies aim to use advanced categorization and personalization to address a long-standing challenge: helping customers find what they want in vast inventories.³

Spotify

Spotify is one of the most well-known companies that leverages personalization by analyzing vast amounts of engagement data, from the songs, podcasts, and audiobooks users consume to when and how they discover them.

Its extensive tagging system (covering genre, mood, tempo, and more) enables the platform to build playlists that reflect individual listening habits. Constant experimentation through micro tests helps Spotify refine its recommendations over time.

This commitment to highly personalized experiences has fueled growth, with its user base and revenues each increasing more than tenfold in the past decade, reaching over 600 million users and $14 billion in annual revenue.⁴

Ulta Beauty

Ulta Beauty utilizes artificial intelligence to enhance personalization by analyzing customer shopping habits and tailoring marketing efforts to each individual. Since 2018, the company has applied AI to capture the dynamic and highly individual nature of beauty, ensuring that no experience feels one-size-fits-all.

In-store, Ulta is piloting LUUM Precision Lash, an automated robot that applies lashes for customers, making a difficult beauty step easier and more accessible. These innovations position Ulta Beauty as a brand that combines technology with retail expertise to create more personal and convenient customer experiences.⁵

Challenges of leveraging hyper-personalized marketing

While effective, hyper-personalization involves some risks:

• Data privacy: Heavy reliance on first-party data, third-party data, and contextual data raises concerns about transparency and trust.
• Data complexity: Processing large volumes of data points from browsing history, purchase history, and customer feedback requires advanced analytics and machine learning algorithms.
• Trust management: Over-personalization or intrusive personalized interactions can negatively impact the customer experience.
• Operational costs: Investing in advanced technologies, a personalization engine, and continuous monitoring requires financial and organizational commitment.

How to manage these challenges?

Businesses can mitigate these obstacles by:

• Prioritizing data privacy with clear, transparent policies and consent mechanisms.
• Using advanced analytics and machine learning algorithms to handle valuable data accurately.
• Monitoring customer feedback and post-purchase surveys to adapt personalization strategies.
• Avoiding over-personalization and ensuring that personalized experiences align with actual customer expectations.

Strategies for businesses to leverage hyper-personalization in marketing

• Adopt a data-driven culture: Build a foundation for hyper-personalization by using advanced analytics and machine learning to derive real-time insights.
• Collect data responsibly: Utilize first-party data, contextual information, and customer feedback, prioritizing privacy at all times.
• Leverage advanced segmentation: Group customers based on purchase history, browsing history, and search data to create hyper-personalized marketing campaigns.
• Test and adapt: Introduce personalization strategies gradually, testing personalized content and personalized messages across digital channels.
• Stay responsive to market trends: Monitor changing customer needs and market trends to refine the hyper-personalization strategy.
• Be transparent: Prioritize trust by ensuring that customers understand how their data is used, helping to maintain long-term loyalty.

External Links

• 1. https://nielseniq.com/global/en/insights/analysis/2024/how-gen-z-consumer-behavior-is-reshaping-retail/
• 2. https://aws.amazon.com/ai/generative-ai/use-cases/personalization/
• 3. https://www.wsj.com/tech/etsy-ebay-aim-to-imitate-social-media-with-hyper-personalized-shopping-92bd28c9
• 4. https://hbr.org/2024/11/personalization-done-right
• 5. https://www.axios.com/2025/03/12/axios-event-technology-retail-personalization